title: Expiratory model-based method to monitor ARDS disease state.
abstract: BACKGROUND
  Model-based methods can be used to characterise patient-specific condition and response to mechanical ventilation (MV) during treatment for acute respiratory distress syndrome (ARDS). Conventional metrics of respiratory mechanics are based on inspiration only, neglecting data from the expiration cycle. However, it is hypothesised that expiratory data can be used to determine an alternative metric, offering another means to track patient condition and guide positive end expiratory pressure (PEEP) selection.
  
  
  METHODS
  Three fully sedated, oleic acid induced ARDS piglets underwent three experimental phases. Phase 1 was a healthy state recruitment manoeuvre. Phase 2 was a progression from a healthy state to an oleic acid induced ARDS state. Phase 3 was an ARDS state recruitment manoeuvre. The expiratory time-constant model parameter was determined for every breathing cycle for each subject. Trends were compared to estimates of lung elastance determined by means of an end-inspiratory pause method and an integral-based method. All experimental procedures, protocols and the use of data in this study were reviewed and approved by the Ethics Committee of the University of Liege Medical Faculty.
  
  
  RESULTS
  The overall median absolute percentage fitting error for the expiratory time-constant model across all three phases was less than 10 %; for each subject, indicating the capability of the model to capture the mechanics of breathing during expiration. Provided the respiratory resistance was constant, the model was able to adequately identify trends and fundamental changes in respiratory mechanics.
  
  
  CONCLUSIONS
  Overall, this is a proof of concept study that shows the potential of continuous monitoring of respiratory mechanics in clinical practice. Respiratory system mechanics vary with disease state development and in response to MV settings. Therefore, titrating PEEP to minimal elastance theoretically results in optimal PEEP selection. Trends matched clinical expectation demonstrating robustness and potential for guiding MV therapy. However, further research is required to confirm the use of such real-time methods in actual ARDS patients, both sedated and spontaneously breathing.
authors.affiliation: University of Canterbury, 8041, Christchurch, New Zealand.
authors.forename: Erwin J
authors.initials: EJ
authors.lastname: van Drunen
authors.forename: Yeong Shiong
authors.initials: YS
authors.lastname: Chiew
authors.forename: J Geoffrey
authors.initials: JG
authors.lastname: Chase
authors.forename: Geoffrey M
authors.initials: GM
authors.lastname: Shaw
authors.forename: Bernard
authors.initials: B
authors.lastname: Lambermont
authors.forename: Nathalie
authors.initials: N
authors.lastname: Janssen
authors.forename: Nor Salwa
authors.initials: NS
authors.lastname: Damanhuri
authors.forename: Thomas
authors.initials: T
authors.lastname: Desaive
mesh_terms: D018450:Disease Progression; D045853:Exhalation; D006801:Humans; D008954:Models, Biological; D057285:Precision Medicine; D012121:Respiration, Artificial; D012128:Respiratory Distress Syndrome / Q000503:physiopathology* / Q000628:therapy; D013997:Time Factors